OpenRaiser/Intern-Atlas huggingface dataset:
Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition
Venue: arXiv cs.LG 2024
Year: 2024

Continual learning (CL) has spurred the development of several methods aimed at consolidating previous knowledge across sequential learning. Yet, the evaluations of these methods have primarily focused on the final output, such as changes in the accuracy of predicted classes, overlooking the issue of representational forgetting within the model. In this paper, we propose a novel representation-based evaluation framework for CL models. This approach involves gathering internal representations from throughout the continual learning process and formulating three-dimensional tensors. The tensors are formed by stacking representations, such as layer activations, generated from several inputs and model `snapshots', throughout the learning process. By conducting tensor component analysis (TCA), we aim to uncover meaningful patterns about how the internal representations evolve, expecting to highlight the merits or shortcomings of examined CL strategies. We conduct our analyses across different model architectures and importance-based continual learning strategies, with a curated task selection. While the results of our approach mirror the difference in performance of various CL strategies, we found that our methodology did not directly highlight specialized clusters of neurons, nor provide an immediate understanding the evolution of filters. We believe a scaled down version of our approach will provide insight into the benefits and pitfalls of using TCA to study continual learning dynamics.
A survey to measure cognitive biases influencing mobility choices
Venue: arXiv cs.CY 2024
Year: 2024

In this paper, we describe a survey about the perceptions of 4 mobility modes (car, bus, bicycle, walking) and the preferences of users for 6 modal choice factors. This survey has gathered 650 answers in 2023, that are published as open data. In this study, we analyse these results to highlight the influence of 3 cognitive biases on mobility decisions: halo bias, choice-supportive bias, and reactance. These cognitive biases are proposed as plausible explanations of the observed behaviour, where the population tends to stick to individual cars despite urban policies aiming at favouring soft mobility. This model can serve as the basis for a simulator of mobility decisions in a virtual town, and the gathered data can be used to initialise this population with realistic attributes. Work is ongoing to design a simulation-based serious game where the player takes the role of an urban manager faced with planning choices to make their city more sustainable.
Efficient computation of Katz centrality for very dense networks via negative parameter Katz
Venue: arXiv cs.SI 2024
Year: 2024

Katz centrality (and its limiting case, eigenvector centrality) is a frequently used tool to measure the importance of a node in a network, and to rank the nodes accordingly. One reason for its popularity is that Katz centrality can be computed very efficiently when the network is sparse, i.e., having only $O(n)$ edges between its $n$ nodes. While sparsity is common in practice, in some applications one faces the opposite situation of a very dense network, where only $O(n)$ potential edges are missing with respect to a complete graph. We explain why and how, even for very dense networks, it is possible to efficiently compute the ranking stemming from Katz centrality for unweighted graphs, possibly directed and possibly with loops, by working on the complement graph. Our approach also provides an interpretation, regardless of sparsity, of "Katz centrality with negative parameter" as usual Katz centrality on the complement graph. For weighted graphs, we provide instead an approximation method that is based on removing sufficiently many edges from the network (or from its complement), and we give sufficient conditions for this approximation to provide the correct ranking. We include numerical experiments to illustrate the advantages of the proposed approach.
WorldQA: Multimodal World Knowledge in Videos through Long-Chain Reasoning
Venue: arXiv cs.CV 2024
Year: 2024

Multimodal information, together with our knowledge, help us to understand the complex and dynamic world. Large language models (LLM) and large multimodal models (LMM), however, still struggle to emulate this capability. In this paper, we present WorldQA, a video understanding dataset designed to push the boundaries of multimodal world models with three appealing properties: (1) Multimodal Inputs: The dataset comprises 1007 question-answer pairs and 303 videos, necessitating the analysis of both auditory and visual data for successful interpretation. (2) World Knowledge: We identify five essential types of world knowledge for question formulation. This approach challenges models to extend their capabilities beyond mere perception. (3) Long-Chain Reasoning: Our dataset introduces an average reasoning step of 4.45, notably surpassing other videoQA datasets. Furthermore, we introduce WorldRetriever, an agent designed to synthesize expert knowledge into a coherent reasoning chain, thereby facilitating accurate responses to WorldQA queries. Extensive evaluations of 13 prominent LLMs and LMMs reveal that WorldRetriever, although being the most effective model, achieved only 70% of humanlevel performance in multiple-choice questions. This finding highlights the necessity for further advancement in the reasoning and comprehension abilities of models. Our experiments also yield several key insights. For instance, while humans tend to perform better with increased frames, current LMMs, including WorldRetriever, show diminished performance under similar conditions. We hope that WorldQA,our methodology, and these insights could contribute to the future development of multimodal world models.
Online Clustering of Known and Emerging Malware Families
Venue: arXiv cs.CR 2024
Year: 2024

Malware attacks have become significantly more frequent and sophisticated in recent years. Therefore, malware detection and classification are critical components of information security. Due to the large amount of malware samples available, it is essential to categorize malware samples according to their malicious characteristics. Clustering algorithms are thus becoming more widely used in computer security to analyze the behavior of malware variants and discover new malware families. Online clustering algorithms help us to understand malware behavior and produce a quicker response to new threats. This paper introduces a novel machine learning-based model for the online clustering of malicious samples into malware families. Streaming data is divided according to the clustering decision rule into samples from known and new emerging malware families. The streaming data is classified using the weighted k-nearest neighbor classifier into known families, and the online k-means algorithm clusters the remaining streaming data and achieves a purity of clusters from 90.20% for four clusters to 93.34% for ten clusters. This work is based on static analysis of portable executable files for the Windows operating system. Experimental results indicate that the proposed online clustering model can create high-purity clusters corresponding to malware families. This allows malware analysts to receive similar malware samples, speeding up their analysis.
Efficient Symbolic Planning with Views
Venue: arXiv cs.RO 2024
Year: 2024

Robotic planning systems model spatial relations in detail as these are needed for manipulation tasks. In contrast to this, other physical attributes of objects and the effect of devices are usually oversimplified and expressed by abstract compound attributes. This limits the ability of planners to find alternative solutions. We propose to break these compound attributes down into a shared set of elementary attributes. This strongly facilitates generalization between different tasks and environments and thus helps to find innovative solutions. On the down-side, this generalization comes with an increased complexity of the solution space. Therefore, as the main contribution of the paper, we propose a method that splits the planning problem into a sequence of views, where in each view only an increasing subset of attributes is considered. We show that this view-based strategy offers a good compromise between planning speed and quality of the found plan, and discuss its general applicability and limitations.
Provably Unlearnable Data Examples
Venue: arXiv cs.LG 2024
Year: 2024

The exploitation of publicly accessible data has led to escalating concerns regarding data privacy and intellectual property (IP) breaches in the age of artificial intelligence. To safeguard both data privacy and IP-related domain knowledge, efforts have been undertaken to render shared data unlearnable for unauthorized models in the wild. Existing methods apply empirically optimized perturbations to the data in the hope of disrupting the correlation between the inputs and the corresponding labels such that the data samples are converted into Unlearnable Examples (UEs). Nevertheless, the absence of mechanisms to verify the robustness of UEs against uncertainty in unauthorized models and their training procedures engenders several under-explored challenges. First, it is hard to quantify the unlearnability of UEs against unauthorized adversaries from different runs of training, leaving the soundness of the defense in obscurity. Particularly, as a prevailing evaluation metric, empirical test accuracy faces generalization errors and may not plausibly represent the quality of UEs. This also leaves room for attackers, as there is no rigid guarantee of the maximal test accuracy achievable by attackers. Furthermore, we find that a simple recovery attack can restore the clean-task performance of the classifiers trained on UEs by slightly perturbing the learned weights. To mitigate the aforementioned problems, in this paper, we propose a mechanism for certifying the so-called $(q, η)$-Learnability of an unlearnable dataset via parametric smoothing. A lower certified $(q, η)$-Learnability indicates a more robust and effective protection over the dataset. Concretely, we 1) improve the tightness of certified $(q, η)$-Learnability and 2) design Provably Unlearnable Examples (PUEs) which have reduced $(q, η)$-Learnability.
Enhancing Q-Learning with Large Language Model Heuristics
Venue: arXiv cs.LG 2024
Year: 2024

Q-learning excels in learning from feedback within sequential decision-making tasks but often requires extensive sampling to achieve significant improvements. While reward shaping can enhance learning efficiency, non-potential-based methods introduce biases that affect performance, and potential-based reward shaping, though unbiased, lacks the ability to provide heuristics for state-action pairs, limiting its effectiveness in complex environments. Large language models (LLMs) can achieve zero-shot learning for simpler tasks, but they suffer from low inference speeds and occasional hallucinations. To address these challenges, we propose \textbf{LLM-guided Q-learning}, a framework that leverages LLMs as heuristics to aid in learning the Q-function for reinforcement learning. Our theoretical analysis demonstrates that this approach adapts to hallucinations, improves sample efficiency, and avoids biasing final performance. Experimental results show that our algorithm is general, robust, and capable of preventing ineffective exploration.
VACO: a Multi-perspective Development of a Therapeutic and Motivational Virtual Robotic Agent for Concentration for children with ADHD
Venue: arXiv cs.HC 2024
Year: 2024

In this work, we present (i) a novel approach how artificial intelligence can support in the therapy for better concentration of children with Attention Deficit Hyperactivity Disorder (ADHD) through motivational attention training with a virtual robotic agent and (ii) a development process in which different stakeholders are included with their perspectives. Therefore, we present three participative approaches to include the perspectives of different stakeholders. An online survey (Study I) was conducted with parents in Germany with the aim of ascertaining whether they would use software to promote their children's attention, what influences their attitude towards using it, and what requirements it would have to meet. About half of the parents would be willing to use software to promote attention. To develop the software as close to practice as possible, one of the developers took part in an intensive training for ADHD with the aim of testing which of the elements are technically feasible. Afterward, a first prototype was presented to clinicians (Study II) to make further adjustments. A first feasibility test (Study III) was conducted with the end users to check if the system works and if children and adolescents can use it. Attentional performance software offers multiple opportunities in the treatment of ADHD if the system is adapted to the needs of the practitioner and end user. This development process requires a lot of time and close interdisciplinary collaboration.
Pinching Tactile Display: A Cloth that Changes Tactile Sensation by Electrostatic Adsorption
Venue: arXiv cs.HC 2024
Year: 2024

Haptic displays play an important role in enhancing the sense of presence in VR and telepresence. Displaying the tactile properties of fabrics has potential in the fashion industry, but there are difficulties in dynamically displaying different types of tactile sensations while maintaining their flexible properties. The vibrotactile stimulation of fabrics is an important element in the tactile properties of fabrics, as it greatly affects the way a garment feels when rubbed against the skin. To dynamically change the vibrotactile stimuli, many studies have used mechanical actuators. However, when combined with fabric, the soft properties of the fabric are compromised by the stiffness of the actuator. In addition, because the vibration generated by such actuators is applied to a single point, it is not possible to provide a uniform tactile sensation over the entire surface of the fabric, resulting in an uneven tactile sensation. In this study, we propose a Pinching Tactile Display: a conductive cloth that changes the tactile sensation by controlling electrostatic adsorption. By controlling the voltage and frequency applied to the conductive cloth, different tactile sensations can be dynamically generated. This makes it possible to create a tactile device in which tactile sensations are applied to the entire fabric while maintaining the thin and soft characteristics of the fabric. As a result, users could experiment with tactile sensations by picking up and rubbing the fabric in the same way they normally touch it. This mechanism has the potential for dynamic tactile transformation of soft materials.
Embedded Distributed Inference of Deep Neural Networks: A Systematic Review
Venue: arXiv cs.DC 2024
Year: 2024

Embedded distributed inference of Neural Networks has emerged as a promising approach for deploying machine-learning models on resource-constrained devices in an efficient and scalable manner. The inference task is distributed across a network of embedded devices, with each device contributing to the overall computation by performing a portion of the workload. In some cases, more powerful devices such as edge or cloud servers can be part of the system to be responsible of the most demanding layers of the network. As the demand for intelligent systems and the complexity of the deployed neural network models increases, this approach is becoming more relevant in a variety of applications such as robotics, autonomous vehicles, smart cities, Industry 4.0 and smart health. We present a systematic review of papers published during the last six years which describe techniques and methods to distribute Neural Networks across these kind of systems. We provide an overview of the current state-of-the-art by analysing more than 100 papers, present a new taxonomy to characterize them, and discuss trends and challenges in the field.
On the (In-)Completeness of Destructive Equality Resolution in the Superposition Calculus
Venue: arXiv cs.LO 2024
Year: 2024

Bachmair's and Ganzinger's abstract redundancy concept for the Superposition Calculus justifies almost all operations that are used in superposition provers to delete or simplify clauses, and thus to keep the clause set manageable. Typical examples are tautology deletion, subsumption deletion, and demodulation, and with a more refined definition of redundancy joinability and connectedness can be covered as well. The notable exception is Destructive Equality Resolution, that is, the replacement of a clause $x \not\approx t \lor C$ with $x \notin \mathrm{vars}(t)$ by $C\{x \mapsto t\}$. This operation is implemented in state-of-the-art provers, and it is clearly useful in practice, but little is known about how it affects refutational completeness. We demonstrate on the one hand that the naive addition of Destructive Equality Resolution to the standard abstract redundancy concept renders the calculus refutationally incomplete. On the other hand, we present several restricted variants of the Superposition Calculus that are refutationally complete even with Destructive Equality Resolution.
Statistical Edge Detection And UDF Learning For Shape Representation
Venue: arXiv cs.CV 2024
Year: 2024

In the field of computer vision, the numerical encoding of 3D surfaces is crucial. It is classical to represent surfaces with their Signed Distance Functions (SDFs) or Unsigned Distance Functions (UDFs). For tasks like representation learning, surface classification, or surface reconstruction, this function can be learned by a neural network, called Neural Distance Function. This network, and in particular its weights, may serve as a parametric and implicit representation for the surface. The network must represent the surface as accurately as possible. In this paper, we propose a method for learning UDFs that improves the fidelity of the obtained Neural UDF to the original 3D surface. The key idea of our method is to concentrate the learning effort of the Neural UDF on surface edges. More precisely, we show that sampling more training points around surface edges allows better local accuracy of the trained Neural UDF, and thus improves the global expressiveness of the Neural UDF in terms of Hausdorff distance. To detect surface edges, we propose a new statistical method based on the calculation of a $p$-value at each point on the surface. Our method is shown to detect surface edges more accurately than a commonly used local geometric descriptor.
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
Venue: arXiv cs.LG 2024
Year: 2024

Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that, typically, multiple annotators, e.g., crowdworkers, provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eight state-of-the-art approaches on eleven datasets with noisy class labels provided either by human or simulated annotators. Our code is publicly available through our repository at https://github.com/ies-research/annot-mix.
A real-time, robust and versatile visual-SLAM framework based on deep learning networks
Venue: arXiv cs.RO 2024
Year: 2024

This paper explores how deep learning techniques can improve visual-based SLAM performance in challenging environments. By combining deep feature extraction and deep matching methods, we introduce a versatile hybrid visual SLAM system designed to enhance adaptability in challenging scenarios, such as low-light conditions, dynamic lighting, weak-texture areas, and severe jitter. Our system supports multiple modes, including monocular, stereo, monocular-inertial, and stereo-inertial configurations. We also perform analysis how to combine visual SLAM with deep learning methods to enlighten other researches. Through extensive experiments on both public datasets and self-sampled data, we demonstrate the superiority of the SL-SLAM system over traditional approaches. The experimental results show that SL-SLAM outperforms state-of-the-art SLAM algorithms in terms of localization accuracy and tracking robustness. For the benefit of community, we make public the source code at https://github.com/zzzzxxxx111/SLslam.
Improved Forward-Forward Contrastive Learning
Venue: arXiv cs.LG 2024
Year: 2024

The backpropagation algorithm, or backprop, is a widely utilized optimization technique in deep learning. While there's growing evidence suggesting that models trained with backprop can accurately explain neuronal data, no backprop-like method has yet been discovered in the biological brain for learning. Moreover, employing a naive implementation of backprop in the brain has several drawbacks. In 2022, Geoffrey Hinton proposed a biologically plausible learning method known as the Forward-Forward (FF) algorithm. Shortly after this paper, a modified version called FFCL was introduced. However, FFCL had limitations, notably being a three-stage learning system where the final stage still relied on regular backpropagation. In our approach, we address these drawbacks by eliminating the last two stages of FFCL and completely removing regular backpropagation. Instead, we rely solely on local updates, offering a more biologically plausible alternative.
Motion Planning under Uncertainty: Integrating Learning-Based Multi-Modal Predictors into Branch Model Predictive Control
Venue: arXiv cs.RO 2024
Year: 2024

In complex traffic environments, autonomous vehicles face multi-modal uncertainty about other agents' future behavior. To address this, recent advancements in learningbased motion predictors output multi-modal predictions. We present our novel framework that leverages Branch Model Predictive Control(BMPC) to account for these predictions. The framework includes an online scenario-selection process guided by topology and collision risk criteria. This efficiently selects a minimal set of predictions, rendering the BMPC realtime capable. Additionally, we introduce an adaptive decision postponing strategy that delays the planner's commitment to a single scenario until the uncertainty is resolved. Our comprehensive evaluations in traffic intersection and random highway merging scenarios demonstrate enhanced comfort and safety through our method.